Regression:

Regression is a statistical method that allows us to model the relationship between a dependent variable and one or more independent variables. The dependent variable is the variable that we are trying to predict, while the independent variables are the variables that we believe influence the dependent variable.

Logistics Regression :

Logistic regression is a statistical model that models the probability of a binary outcome, such as yes or no, based on prior observations of a data set. It is a supervised learning algorithm, which means that it learns from a set of labeled data, where the output variable is the binary variable that we are trying to predict.

Logistic regression models are trained using a maximum likelihood approach, which means that the model parameters are chosen to maximize the probability of the observed data. Once trained, logistic regression models can be used to predict the probability of the output variable for new values of the input variables.

Logistic regression is a powerful tool that is used in a wide variety of fields, including finance, marketing, and healthcare. For example, logistic regression models can be used to:

  • Predict the probability of a customer making a purchase
  • Predict the probability of a patient having a disease
  • Predict the probability of a loan defaulting
  • Predict the probability of a student passing an exam

 

Here is an example of a simple logistic regression model:

P(z) = 1 / (1 + e^(-z))

The slope of the logistic regression curve tells us how much the probability of the output variable changes for a one-unit change in the independent variable. The y-intercept of the logistic regression curve tells us the probability of the output variable when the independent variable is equal to zero.

Here are some examples of how logistic regression is used in the real world:

  • Finance: Logistic regression models can be used to predict the probability of a loan defaulting, the probability of a customer churning, or the probability of a stock price going up or down.
  • Marketing: Logistic regression models can be used to predict the probability of a customer clicking on an ad, the probability of a customer making a purchase, or the probability of a customer responding to a marketing campaign.
  • Healthcare: Logistic regression models can be used to predict the probability of a patient having a disease, the probability of a patient responding to a treatment, or the probability of a patient being readmitted to the hospital.